Stochastic and Distributed Optimization for Energy Systems

Stochastic and Distributed Optimization for Energy Systems



The increasing need for the de-carbonization of energy supply calls for new operational methods for power and energy systems. In this context, tailored system and control approaches are pivotal. The specific challenges include the consideration of volatile renewable generation, uncertain forecasts thereof, and the cyber-physical nature of energy grids.

In this talk, we focus on the Optimal Power Flow (OPF) problem, which refers to a class of large-scale non-convex steady-state optimization problems frequently arising in power systems. For example, OPF problems provide optimal set points for power dispatch that satisfy the power flow equations and technical limitations such as generation and/or transmission limits.

However, OPF problems are highly non-convex and subject to considerable uncertainties, which includes forecasts of renewable generation and household consumption, line parameters and grid topology. Due to their large-scale nature, the distributed solution of OPF problems is also subject to considerable research efforts.

In the first part of this talk, we will discuss recent developments for stochastic and distributed OPF. We provide an overview of the state of the art towards uncertainties in OPF problems and comment on their bottlenecks. Moreover, we will discuss the concept of Polynomial Chaos Expansions (PCE) which allows to consider non-Gaussian uncertainties in OPF problems [1,2].  

The second part of this talk will be centered on distributed algorithms for OPF. We will comment on the Augmented Direction of Multipliers Method (ADMM) and on recent results on the Augmented Lagrangian based Alternating Direction Inexact Newton method (ALADIN) and its application to OPF problems [3,4,5].



[1] Mühlpfordt, T., Faulwasser, T., & Hagenmeyer, V. (2018). A generalized framework for chance-constrained optimal power flow. Sustainable Energy, Grids and Networks, 16, 231-242.

[2] Mühlpfordt, T., Roald, L., Hagenmeyer, V., Faulwasser, T., & Misra, S. (2019). Chance-Constrained AC Optimal Power Flow--A Polynomial Chaos Approach. IEEE Transactions on Power Systems.

[3] Houska, B., Frasch, J., & Diehl, M. (2016). An augmented Lagrangian based algorithm for distributed nonconvex optimization. SIAM Journal on Optimization, 26(2), 1101-1127.

[4] Engelmann, A., Jiang, Y., Mühlpfordt, T., Houska, B., & Faulwasser, T. (2018). Toward distributed OPF using ALADIN. IEEE Transactions on Power Systems, 34(1), 584-594.

[5] Engelmann, A., Jiang, Y., Houska, B., & Faulwasser, T. (2019). Decomposition of non-convex optimization via bi-level distributed ALADIN. arXiv preprint arXiv:1903.11280.


Timm Faulwasser has studied Engineering Cybernetics at the University Stuttgart, with majors in systems and control and philosophy. From 2008-2012 he was a member of the International Max Planck Research School for Analysis, Design and Optimization in Chemical and Biochemical Process Engineering Magdeburg. In 2012 he obtained his PhD (with distinction) from Faculty of Electrical Engineering and Information Engineering, Otto-von-Guericke University Magdeburg, Germany. 2013-2016 he was with the Laboratoire d’Automatique, Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland. 2015-2019, he was leading the Optimization and Control Group at the Institute for Automation and Applied Informatics at Karlsruhe Institute for Technology (KIT). Since November 2019 he holds the professorship for Energy Efficiency at TU Dortmund University, Germany.

His main research interests are optimization-based and predictive control of nonlinear systems with applications in energy systems, mechatronics/robotics, physics, process systems engineering and climate economics.

Date & time

3–4pm 4 Feb 2020



Professor Timm Faulwasser



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